This paper makes two scientific contributions to the field of
exoskeleton-based action and movement recognition. First, it presents a novel
machine learning and pattern recognition-based framework that can detect a wide
range of actions and movements - walking, walking upstairs, walking downstairs,
sitting, standing, lying, stand to sit, sit to stand, sit to lie, lie to sit,
stand to lie, and lie to stand, with an overall accuracy of 82.63%. Second, it
presents a comprehensive comparative study of different learning approaches -
Random Forest, Artificial Neural Network, Decision Tree, Multiway Decision
Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Decision Stump,
Auto MLP, Linear Regression, Vector Linear Regression, Random Tree, Na\"ive
Bayes, Na\"ive Bayes (Kernel), Linear Discriminant Analysis, Quadratic
Discriminant Analysis, and Deep Learning applied to this framework. The
performance of each of these learning approaches was boosted by using the
AdaBoost algorithm, and the Cross Validation approach was used for training and
testing. The results show that in boosted form, the k- NN classifier
outperforms all the other boosted learning approaches and is, therefore, the
optimal learning method for this purpose. The results presented and discussed
uphold the importance of this work to contribute towards augmenting the
abilities of exoskeleton-based assisted and independent living of the elderly
in the future of Internet of Things-based living environments, such as Smart
Homes. As a specific use case, we also discuss how the findings of our work are
relevant for augmenting the capabilities of the Hybrid Assistive Limb
exoskeleton, a highly functional lower limb exoskeleton.